System and method for determining vehicle component conditions

ABSTRACT

A system and method for determining vehicle component conditions is provided. A predictive model is built for a vehicle component and values are mapped for a feature of the vehicle component using the predictive model. A threshold is applied to the mapped values. An occurrence of a fault of the vehicle component is predicted when one or more of the mapped values exceeds the threshold and an extended optimal interval during which the fault is predicted to occur is identified.

FIELD

This application relates in general to vehicle maintenance, and inparticular, to a system and method for determining vehicle componentconditions.

BACKGROUND

As the population increases across parts of the world, many cities areforced to deal with high traffic volumes, which results in trafficcongestion. In an attempt to ease the congestion, many cities areworking on implementing shared passenger transportation services,including rapid transit systems, trains, monorails, trams, light rails,and other types of commuter rail systems. Use of shared transportationcan reduce the number of vehicles on the roads, which in turn lessensthe traffic congestion.

Although public transportation provides a popular option for reducingtraffic, the vehicles can be expensive to maintain, including labor andparts replacement. For example, most public transportation vehicles,such as trains and busses, include doors that open and shut to allowpassengers to enter or exit the vehicle. The doors must be regularlymaintained to prevent unexpected malfunctioning, which may result inunscheduled downtime of the vehicle for repair, disruption of passengerpick-up schedules for the vehicle, and customer dissatisfaction.

Generally, maintenance is scheduled based on manufacturer guidance orlab testing. Yet, utilizing only the guidelines and lab testing forscheduling maintenance can fail to provide accurate results based on anactual condition of the vehicle, which results in unnecessarymaintenance, such as changing parts that are still working. Currently,studies have been performed to assess a condition of automatic traindoors using Vibrational Analysis for Remote Condition Monitoring.Specifically, vibrations of a door are measured as the door is moving toan open or closed position and the measurements are used to determinewhich components are likely to develop faults. However, othermeasurements, such as the door motor current, can be used to moreaccurately and specifically identify different conditions of the door,some of which are separate from the components, such as a lack ofgrease, an object stuck in the door, or an excess of dirt.

Therefore, there is a need for an approach to accurately identifycurrent and future component conditions to improve the effectiveness ofscheduled maintenance and to identify and prevent unexpected componentfailure.

SUMMARY

Public and private transportation companies, as well as individuals,generally schedule maintenance for their vehicles based onrecommendations by the vehicle manufacturer, dealer, or mechanic. Therecommendations can help prevent unexpected maintenance failure;however, the recommendations can be overly cautious and require an ownerof the vehicle to unnecessarily perform maintenance. A more accuratedetermination of when maintenance is necessary, such as based onpredicted maintenance failures, can save vehicle owners large amounts oftime and money. Further, being able to identify a particular conditionof a vehicle component without an inspection helps the owner to savemoney and time.

An embodiment provides a method for determining vehicle componentconditions via performance correlation. A list of doors for maintenanceon a transport vehicle is maintained. Measurements for one of the doorsbased on an inspection of that door are maintained. A determination ismade as to whether maintenance is required for the door based on themeasurements and a maintenance status is assigned to the door. The doormeasurements are compared to measurements for other doors of thetransportation vehicle. Those other doors with measurements similar tothe door are identified and the maintenance status of the door isassigned to the other doors identified.

A further embodiment provides a system and method for determiningvehicle component conditions. A predictive model is built for a vehiclecomponent, and values are mapped for a feature of the vehicle componentusing the predictive model. A threshold is applied to the mapped values.An occurrence of a fault of the vehicle component is predicted when oneor more of the mapped values exceeds the threshold, and an extendedoptimal interval during which the fault is predicted to occur isidentified.

Still other embodiments of the present invention will become readilyapparent to those skilled in the art from the following detaileddescription, wherein is described embodiments of the invention by way ofillustrating the best mode contemplated for carrying out the invention.As will be realized, the invention is capable of other and differentembodiments and its several details are capable of modifications invarious obvious respects, all without departing from the spirit and thescope of the present invention. Accordingly, the drawings and detaileddescription are to be regarded as illustrative in nature and not asrestrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing a system for determining doorconditions, in accordance with one embodiment.

FIG. 2 is a flow diagram showing a method for determining doorconditions, in accordance with one embodiment.

FIG. 3 is a flow diagram showing, by way of example, a process fortraining a classifier.

FIG. 4 is a block diagram showing, by way of example, a confusion matrixfor a multi-class fault classification.

FIG. 5 is a block diagram showing, by way of example, a revisedconfusion matrix for the multi-class fault classification of FIG. 4.

FIG. 6 is a flow diagram showing, by way of example, methods foranalyzing operational data.

FIG. 7 is a flow diagram showing, by way of example, a process forcorrelating different measurements for a door.

FIG. 8 is a block diagram showing, by way of example, a graphcorrelating different measurements for different doors.

FIG. 9 is a block diagram showing, by way of example, a graph of one ofthe measurements from the graph of FIG. 8 for two of the doors.

FIG. 10 is a flow diagram showing, by way of example, a process fordetermining whether a correlation exists between doors on a commontransportation vehicle.

FIG. 11 is a block diagram showing, by way of example, two separategraphs each representative of correlation data for a pair of doors.

FIG. 12 is a flow diagram showing, by way of example, a process forefficiently scheduling maintenance appointments.

FIG. 13 is a flow diagram showing, by way of example, a process fordetecting outliers.

FIG. 14 is a flow diagram showing, by way of example, a process forpredicting failure of a door.

FIG. 15 is a flow diagram showing, by way of example, a process forreducing unnecessary maintenance based on predictive faults.

FIG. 16 is a block diagram showing, by way of example, a graph ofaverage predicted values for a component feature over time.

DETAILED DESCRIPTION

As cities experience high levels of traffic congestion, many individualcommuters are turning to public transportation, or mass transit, forcommuting to and from work or school. Different types of publictransportation include rapid transit systems, trains, monorails, trams,and light rails. Generally, public transportation vehicles includevehicle components that are frequently used and require maintenance toprevent unexpected failure. For example, each type of transportationvehicle includes at least one set of doors, which open and closenumerous times a day to allow passengers to enter and exit the vehicle.To prevent malfunctioning, maintenance appointments are usuallyscheduled based on recommendations by the vehicle manufacturer ormechanic. Most recommendations are overly cautious and may requireunnecessary inspection, which can be a large expense to an owner of thetransportation vehicles for both time and money.

Accurately identifying a condition of a door, including a particularproblem or fault, as well as when the fault is likely to occur, providesa valuable benefit to the transportation vehicle owners by saving timeand money. FIG. 1 is a block diagram showing a system 10 for determiningdoor conditions, in accordance with one embodiment. Measurements for acomponent 12 on a transportation vehicle 11, such as a publictransportation vehicle or individual vehicle can be obtained. In oneembodiment, the component can be the doors 12 of the vehicle 11 and themeasurements can include a sequence of data points for motor currentvalues of the door and door positions, which are collected at successivetime instants as time series data. The measurements can be associatedwith open and close commands for the door, and an operating state. Othercomponents and measurements are possible. Once obtained, themeasurements can be stored as operational data 18, in a database 14interconnected to a server 13. The server 13 and database 14 can bemaintained by an owner of the transportation vehicle or a company thatmanages the transportation vehicle. The server 13 can transmit the datato a condition server 19 via an internetwork, such as the Internet, foranalysis, including determining a particular condition of each door,predicting condition based maintenance, and identifying doorsimilarities for maintenance purposes.

A further set of measurements can also be obtained from thetransportation vehicle and stored in the database 14 with determinedconditions of the vehicle doors, as laboratory data. The conditions canbe assigned by a mechanic who has inspected the door, obtainedmeasurements, and then diagnosed the condition. Alternatively, thecondition can be automatically assigned. The laboratory data can also betransmitted to the conditions server 19 for use in helping identifying acondition for the operational data.

Specifically, the laboratory data 27 and the operational data 22 can bestored in a database 20 interconnected to the condition server 19. Thecondition server 19 can include a feature module 23, training module 24,classifier 25, predictor module 26, and correlation module 27. Thefeature module 23 can analyze both the laboratory data 21 andoperational data 22 to determine features from the data, such as a mean,maximum value, minimum value, standard deviation, duration, secondhighest peak, and second lowest minimum. Other types of features arepossible.

Once determined, the training module 24 utilizes the features for thelaboratory data 21 to train the classifier 25 along with the known doorconditions associated with those features, as further described belowwith reference to FIG. 3. The training module 24 also uses thelaboratory data 21 to test the classifier 25 and ensure that theclassifier accurately classifies the operational data 22, as furtherdescribed below with reference to FIGS. 4 and 5. After training iscomplete, the features of the operational data 22 are provided to theclassifier 25 and a condition of the door can be diagnosed.

In addition to diagnosing a current door condition, maintenance needscan also be predicted. The prediction module 26 identifies long termtrends of a transportation vehicle component and determines featuresthat are predictive of certain kinds of faults, as further describedbelow with reference to FIG. 14. The ability to accurately predictmaintenance needs focuses on condition based maintenance, which canprevent unnecessary maintenance based on manufacturer recommendations.

Finally, the features of a component can also be used to increaseefficiency of maintenance inspections by identifying similaritiesbetween doors, identifying doors that are dissimilar from other doors,and determining correlations among different measurements for thecomponent. Determining similarities between doors of a transportationvehicle and determining which doors are outliers can increasemaintenance efficiency by identifying those doors that requiremaintenance and those doors that do not without the need for a manualinspection of each door.

The client and servers can each include one or more modules for carryingout the embodiments disclosed herein. The modules can be implemented asa computer program or procedure written as source code in a conventionalprogramming language and is presented for execution by the centralprocessing unit as object or byte code. Alternatively, the modules couldalso be implemented in hardware, either as integrated circuitry orburned into read-only memory components, and each of the client andserver can act as a specialized computer. For instance, when the modulesare implemented as hardware, that particular hardware is specialized toperform the data quality assessment and other computers cannot be used.Additionally, when the modules are burned into read-only memorycomponents, the computer storing the read-only memory becomesspecialized to perform the data quality assessment that other computerscannot. The various implementations of the source code and object andbyte codes can be held on a computer-readable storage medium, such as afloppy disk, hard drive, digital video disk (DVD), random access memory(RAM), read-only memory (ROM) and similar storage mediums. Other typesof modules and module functions are possible, as well as other physicalhardware components.

Accurately predicting and identifying component problems saves time andmoney by preventing unexpected component failure, long wait times, andcustomer dissatisfaction. FIG. 2 is a flow diagram showing a method 30for determining door conditions, in accordance with one embodiment.Laboratory data can be optionally obtained (block 31) for a component ofa transportation vehicle and used to train (block 32) a classifier thatcan map operational data to a known condition of the component. In oneembodiment, the component includes vehicle doors, which will be used inthe discussion below, as an example. However, other components arepossible, including switch machines, for moving railway tracks. Thelaboratory data includes measurements of the motor current for one ormore of the doors on the vehicle over time and door position, and anunderlying door condition associated with the motor current values. Inone example, a sample rate of time for the measurements can be 1/10seconds; however, other durations are possible. The door conditions caninclude two classifications, normal or abnormal. An abnormal conditioncan further include specific types of faults, such as an object stuck inthe doors, an obstacle in a bottom rail of the door, bending ordeformity of the bottom rail, loose bolts, the presence of dirt, and alack of grease. A determination of loose bolts can further provide anindication of the particular bolt that is loose.

The operational data is obtained for the component of the transportationvehicle (block 33) and includes only motor current and door positionmeasurements for the component over time, and not the underlying doorconditions, like the laboratory data. Subsequently, the operational datais analyzed (block 34) to detect a current condition of the door (block35), to predict future conditions of the door (block 36), and identifyrelated doors (block 37). In one embodiment, the analysis of theoperational data can include use of laboratory data models from thetrained classifier. For example, the operational data can be provided tothe classifier for identifying one or more conditions for the door.However, if the laboratory data and classifier are not available,alternate analyses can be performed to detect a current door conditionor predict future conditions, such as by identifying a correlationbetween two or more doors, performing a feature trend analysis, andoutlier detection, which are described further below with reference toFIGS. 7, 10, and 13. If a need for maintenance is identified (block 38)based on the assigned or future conditions, a maintenance appointmentcan be scheduled. However, if no maintenance is required, an inspectionof the door need not be performed.

When laboratory data is available for a component, the data can be usedto train a classifier, which can later be used to assign conditions todoors having only operational data. FIG. 3 is a flow diagram showing, byway of example, a process 40 for training a classifier. Laboratory datais analyzed to generate a time series model for the measurements of thedoor via time series segmentation or functional data analysis. Asdescribed above, the laboratory data includes measurements of the motorcurrent for the door and door position, which are collected atsuccessive time instants that are correlated in time, and a condition ofthe door. The time series segmentation includes plotting the motorcurrent measurements against time and dividing the data into multiplesegments, which are representative of different stages of a single openand close cycle for the door. Meanwhile, the functional data analysisincludes plotting the motor current measurements against time for asingle open and close cycle and fitting a curve to the plotted datapoints for motor current measurements. An individual associated with thetransportation vehicle on which the doors are located can select (block41) one of the time series segmentation or functional data analysis forgenerating a time series representation of the laboratory data, oralternatively, one of the methods can be selected automatically or as adefault.

For time series segmentation, uniquely identifiable stages for a dooropen and close cycle are identified as segments. Each segment includes aset of features for a signal of interest, such as the motor current. Inone example, a graph of segments for an open and close cycle of a doorcan be generated with time located along an x-axis and door positionlocated along a y-axis. Door position values for a single open and closecycle, which can be identified by open and close commands that are usedto provide instructions to the door to open or close, can be plottedalong a curve. In one instance, the curve can represent a curve with aflattened top or a modified bell curve.

The open and close cycle can be segmented into different states using asegmentation algorithm that considers the motor current, open and closecommands, and an operating state as input. The different states areplotted along the curve to identify states where the behavior of thedoor is qualitatively different from the other states. In the laboratorydata for the door, each of the open and close commands, and theoperating state can associated with a binary value. For example, a valueof 1 for the open command can represent an open state of the doors,while a value of 0 represents a closed state. Additionally, a value of 1for the closed command represents a closed state, while a value of 0represents an open state. The operating state can be another discretesignal that signifies whether the door is open or closed. This state canbe generated, for example, using a switch that turns on/off based onpresence or lack of physical contact. In one embodiment, six segments ofthe cycle are identified, including state 1 for a closed position, state2 for acceleration, state 3 for constant speed, state 4 for openposition, state 5 for acceleration, state 6 for the rest of the cycletill the door is closed again. Segmentation that results in fewer orgreater number of states is also possible by altering the segmentationrules.

The different states are identified using a set of predetermined rulesthat utilize the motor current, the derivative and sign of the motorcurrent, the operating state, and the open and close commands. Forexample, when an open command is given, the default state 1, signifyinga closed door, transitions to the next state 2. If the current state is2, and the rate of change of current goes from positive to negative,then the state transitions from 2 to 3. Additionally, when an operatingstate indicates the door is closed, the current state can transitionback to the default state 1. Other rules are possible.

However, not all doors have the same measurements and a door withdifferent measurements may be associated with different rules forsegmentation. For example, rules for doors associated with measurementsfor motor current and stroke can include if an open command is given, adefault state transitions to state 1. If the value of the stroke doesnot change, a current state of the door can transition to a stateindicating an open door. Also, if the value of the stroke does notchange over a predetermined number of successive measurements, and achange in the current value for the last n measurements are each lessthan a predetermined threshold, then the current state can transitionback to the default state.

Once identified, the output of the segmentation includes identificationof the different states for an open and close cycle for a single door,which can be plotted on a graph with the motor current values. On thegraph, time can be located along an x-axis and motor current, which canbe measured in Amperes, can be located along a y-axis. Data points formotor current values of the door versus time and data points for thesegmented states that correspond with the motor current data points areplotted on the graph as output for the segmentation.

In a further embodiment, functional data analysis can be applied to thelaboratory data collected from the doors to generate a time seriesrepresentation of the data for a single open and close cycle for thedoor. The motor current measurements and door position can be plottedagainst time samples to generate a profile representative of themeasurements. A curve is then fit to the profile using a Fourier orspline basis function. Smoothness constraints can be applied to thebasis to minimize an impact of noise from the laboratory data. The basishelps identify landmarks for door measurements that can be alignedacross different curves.

Returning to the discussion with respect to FIG. 3, features can bedetermined (block 42) for each state of an open and close cycle from thesegmentation output or for the basis of the open and close cycle. Thefeatures for each segmented state can include a mean, maximum, minimum,standard deviation, and duration, as well as other types of features.The mean measures a mean value of the motor current for a state, whilethe maximum measures a highest value of the motor current in that stateand the minimum measures a lowest value of the motor current for thatstate. The standard deviation measures an amount of variation of themotor current from the mean and the duration measures a time span forthe state. Additionally, the features for the basis can include mean,duration, and a particular extremum.

Once identified, the features can be used to train (block 44) aclassifier with the known conditions for the laboratory data.Specifically, a function is created for each condition that maps thefeatures to that particular door condition. The classifier can beselected (block 43) from support vector machines, neural networks, andensemble classifiers, such as Random Forest classifier. Prior totraining, the features can split into a training data set for trainingthe classifier and a testing data set for determining an accuracy of thetrained classifier. In one embodiment, 60% of the available features canbe used for training, while the remaining 40% can be used for testing.However, other percentage splits are possible. In a further embodiment,k-fold cross validation can be used to determine an amount of thefeature data to be used for training and testing. Specifically, thefeatures are split into k groups. One of the k groups is used as atesting set, while the remaining k−1 groups are used as the trainingset. The process is repeated until each of the k groups has been used asthe testing set. The results of the testing based on each of thek-groups can then be averaged to determine an accuracy of theclassifier.

During training (block 44), n training instances are provided as(X_(i),Y_(i))_(i=1, . . . , n), where X_(i) represents a vector offeatures within each of the current states for the segmented states orfor the basis, and Y_(i) ∈{Normal, Fault₁, . . . , Fault_(m)} representsthe normal or faulty condition of the door. Hereinafter, only featuresof the segmented states will be discussed as an example, although thesame processes can be applied to the features of the basis. The trainingset is then used to learn a model that maps features to a doorcondition. For example, a feature vector X_(test) can be mapped to aclass Y_(test) ∈{Normal, Fault₁, . . . , Fault_(m)}. The differentfaults can include an object stuck in the doors, an obstacle in a bottomrail of the door, bending or deformity of the bottom rail, loose bolts,the presence of dirt, and a lack of grease. Further, for the loose boltfault, a determination of which bolt is loose within the door can bedetermined. In a further embodiment, a two-class classification can beused where Y_(test) ∈{Normal, Abnormal}. The classifier can also learn aposterior probability distribution via k nearest neighbors orgeneralized linear models that indicates a probability of a conditionfor new observations of the door, such as from the operational data.

After training, the testing set is input (block 45) into the classifierto determine an accuracy of the testing set classification.Specifically, the classification accuracy can be defined as a percentageof a total number of feature data for an open and close cycle correctlyclassified across all classes. The results of the testing can berepresented by a confusion matrix. For an n number of traininginstances, the confusion matrix M_(confusion) is a n×n matrix, where theelement M_(confusion)(i,j) at the ith row and jth column is given by thefollowing equation:

$\begin{matrix}{{M_{confusion}\left( {i,j} \right)} = {\frac{{Number}\mspace{14mu}{of}\mspace{14mu}{instances}\mspace{14mu}{class}\mspace{14mu} i\mspace{14mu}{classified}\mspace{14mu}{as}\mspace{14mu}{class}\mspace{14mu} j}{{Total}\mspace{14mu}{number}\mspace{14mu}{of}\mspace{14mu}{instances}\mspace{14mu}{of}\mspace{14mu}{class}\mspace{14mu} i}100}} & \left( {{Eq}.\mspace{11mu} 1} \right)\end{matrix}$

After testing is completed, accuracy of the results is determined. Ifthe results are acceptable (block 46), no further processing of theclassifications need be performed. However, if the results are notacceptable, the classifications can be revised, new features can beselected (block 47), or a new classifier can be selected. Ideally, for atwo class classification, all instances of, for example, normal shouldbe correctly identified as normal, while all instances of abnormalshould be correctly identified. However, small amounts ofmisclassification can be permissible, such as below 5%misclassification. Large amounts of misclassification, if present, canindicate that different classifications, such as faults, can bedifficult to distinguish. For example, FIG. 4 is a block diagramshowing, by way of example, a confusion matrix for a multi-class faultclassification. A list of the classifications are listed horizontally 81along a top of the matrix, as well as vertically 82 on a left side ofthe matrix. The classifications include normal, F1 for an loose bolt, F2for an obstacle in a bottom rail of the door on an outside, F3 for anobstacle stuck in a bottom rail of the door on the top, F4 for an objectin a bottom rail of the door on an inside, and F5 for a lack of grease.Other classifications can be considered such as a bent or deformed railof the door, and object stuck in the door, and a presence of dirt. Thevalues of the matrix 83 represent a percentage that a classification onthe left side of the matrix was correctly or incorrectly identified as aclassification listed along the top of the matrix. For example, in thefirst row, the normal classification was correctly identified as normal95.1% of the time, while the normal classification was incorrectlyidentified as F1 0.5% of the time.

The classifications for normal, F1 and F5 appear highly accurate, asindicated by the percentages of 91.4% and above. However, despite thehigh accuracy, some misclassification occurred. For example, there issome confusion between the normal classification and F1, or fault 1. Inparticular, some fault 1 data is misclassified as normal data. Fault 1corresponds to a loose bolt. In this case, the cause of confusion may bedue to a looseness of the bolt. For example, a bolt that is only alittle loose, may be confused with a tightened bolt; however, thosebolts that are very loose will not be confused. Detecting confusion forother conditions are possible. For example, confusion between a normalstate and when an object is stuck in the door may be due to objects thatare not stiff and the door may not experience a lot of resistance duringopening and closing since the object was not stiff, and was thus,classified as normal. Another reason for the confusion is that theclassifier may assign almost equal probabilities to both conditions ofnormal and fault. To prevent confusion when equal probabilities areassigned, the classifier can also be instructed to output an “uncertain”notification.

In contrast to the highly accurate classifications, the classificationsfor F2, F3, and F4 had accuracy percentages varying from 55.6% to 70.4%,which are not very accurate and indicates that the classifier hastrouble distinguishing between the faults for an obstacle stuck atdifferent positions on the bottom rail of the door. Since each of thefaults corresponds to some kind of bottom rail obstacle, the classes canbe combined in an attempt to obtain higher accuracy results forclassifying an obstacle in the bottom rail of a door versus the otherfaults, such as a loose bolt. FIG. 5 is a block diagram showing, by wayof example, a revised confusion matrix 90 for the multi-class faultclassification of FIG. 4. In the revised confusion matrix 90, the doorconditions are located both horizontally and vertically along the graph.The classifications for F2, F3, and F4 are combined into a singlecondition. As shown by the value for a comparison of the combinedcondition, the accuracy rate for correctly identifying an obstacle in abottom rail of the door is now 98.6%. In a further embodiment, aconfusion matrix can be generated for identifying a specific bolt thatis loose when a loose bolt has been identified for the door. Based onthe classification testing results, some features may be identified asmore predictive of classifying a door for a particular condition.

Once the classifier has been trained and can accurately assignclassifications to doors, operational data can be input into theclassifier for classification of a door. However, in some instances,laboratory data used to train the classifier may not be available. Otherprocesses for analyzing the operational data are possible to identify aproblem or predict a fault. FIG. 6 is a flow diagram showing, by way ofexample, methods 100 for analyzing 101 operational data. The operationaldata can be processed to determine a similarity 105 or dissimilaritybetween doors, a correlation 104 of different measurements for a singledoor, long term trends 103 exhibited by the door, and outliers 102.Identifying a correlation among different measurements 104 can helpidentify whether any new information can be gained and whether seasonaldependencies exist based on correlations with external temperature. FIG.7 is a flow diagram showing, by way of example, a process 110 forcorrelating different measurements for a door. Two differentmeasurements can be identified (block 111) for comparison. A correlationcoefficient of the measurements is determined (block 112) for each dooron the transportation vehicle. The correlation coefficients can bedetermined via Euclidean distance or maximum distance of the measurementvalues. Other methods for determining similarity are possible.

The determined correlation coefficients are then compared (block 113)for all the doors. In one example, the correlation coefficients can becompared via a graph. FIG. 8 is a block diagram showing, by way ofexample, a graph 120 correlating different measurements for differentdoors. The doors of a transportation vehicle are listed along an x-axis121, while a correlation coefficient of two different measurements forthe doors are listed along a y-axis 122. In this example, themeasurements are motor temperature and outside temperature. As describedabove, a correlation coefficient is determined between the motortemperature and outside temperature measurements for each door. Oncedetermined, values 123 for the correlated coefficients are plotted on agraph for each door. Returning to the discussion of FIG. 7, doors thatare correlation exceptions can be identified (block 114). In particular,the correlation exceptions include doors that do not show a strongcorrelation between the measurements when the remaining doors show astrong correlation of the measurements. For example, on the graph 120,the measurements appear to be highly correlated for doors 1, 2, 3, 5, 6,7, and 8, with the exception of door 4. The door exceptions can beidentified based on a threshold, including a standard deviation value.For example, if the correlation coefficient for one door is apredetermined number of standard deviations away from the other doors,then that door is an exception. Alternatively, if the door satisfies athreshold for a measure of dissimilarity from the other doors, then thatdoor is an exception. Other means for determining door exceptions arepossible.

The lack of correlation identified for door 4 may be due to abnormaloutside temperature readings, abnormal motor temperature readings, or afault of the door. Further analysis of the measurements can be performedto determine (block 115) whether at least one of the measurements forthe excepted door 4 is anomalous and if anomalous, inspection ormaintenance can be scheduled for the door.

To further analyze the lack of correlation, the motor temperaturereadings can be compared for door 4 and one of the other doors showing ahigh correlation between the measurements, such as door 1. FIG. 9 is ablock diagram showing, by way of example, a graph 130 of one of themeasurements from the graph of FIG. 8 for two of the doors. Dates arelisted along an x-axis 131 of the graph 130, while motor temperature islisted along a y-axis 132. Data points for the separate motortemperature measurements for door 1 135 and door 4 134 are plotted onthe graph according to the legend 133. On the graph, most of the datapoints 135 for door 1 appear to have lower motor temperature readingsthan the data points 134 for door 4. An anomaly can be determined forthe data using, for example, Robust Principal Component Analysis. Inthis example, the motor temperature readings for door 4 are anomalous.Reasons for the anomaly can include a problem with the temperaturesensor or a problem with the motor itself. However, based on the anomalyidentified for door 4, an inspection can be scheduled to determinewhether the motor is not working correctly or the temperature readingsare inaccurate. Additionally, since the remaining doors are associatedwith correlation coefficients that are similarly related, no furtherinspection of those doors may be necessary.

Other measurements can be analyzed for identifying a correlation, suchas outside temperature with other measurements, including a sum of themotor current during opening of the doors, a maximum current duringopening, a minimum current during opening, a mean current duringopening, and a standard deviation of current during opening. In oneexample, when the sum of the motor current and the mean value of themotor current are compared with the outside temperature, a strongnegative correlation is identified, which indicates that more effort isrequired to open the door during cold weather. The outside temperaturecan also be analyzed with measurements associated with closing of thedoors to determine whether cold weather also affects closing. In oneexample, strong correlations for outside temperature and door closingdid not exist. The correlations are helpful in predicting futuremaintenance.

Correlations between different doors of a same type can also bedetermined to accurately identify doors that may need inspection ormaintenance, and reduce time and money spent on unnecessary scheduledmaintenance appointments. FIG. 10 is a flow diagram showing, by way ofexample, a process 140 for determining whether a correlation existsbetween doors on a shared transportation vehicle. One open and closecycle is selected (block 141) from the operational data for each door tobe analyzed, for each hour and each day over a predetermined timeperiod. A feature vector is then generated (block 142) for each selectedopen and close cycle using the features described above with respect toFIG. 3 that are associated with that open and close cycle. For instance,a feature vector for a door can be represented as:

$\begin{matrix}{{f_{i}\left( {{hour},{month},{year}} \right)} = \left\lbrack {{\max({current})},{\min({current})},{{mean}\left( {{open}\mspace{14mu}{current}} \right)},{{mean}\left( {{closing}\mspace{14mu}{current}} \right)},{{average}\left( {{outside}\mspace{14mu}{temperature}} \right)},{{average}\left( {{motor}\mspace{14mu}{temperature}} \right)}} \right\rbrack} & \left( {{Eq}.\mspace{14mu} 2} \right)\end{matrix}$

The subscript i represents an identification of the door for which thefeature vector applies.

Once generated, each element, or feature, within the feature vectors canbe normalized (block 143). In one embodiment, each element of the vectoris normalized by a maximum value for that element across all open andclose cycles. Two doors, s and t, are selected (block 144) forcomparison. Specifically, for each feature, i, a measure of similaritybetween the vectors on a given day and hour for door s and door t isdetermined (block 145) as a correlation value Corr_(st), using theequation listed below:

$\begin{matrix}{{{Corrst}\left( {{hour},{month},{year}} \right)} = \frac{\sum_{i = l}^{n}{\left( {f_{s_{i}} - {\overset{\_}{f}}_{s_{i}}} \right)\left( {f_{\;_{t_{i}}} - {\overset{\_}{f}}_{t_{i}}} \right)}}{\sqrt{\sum_{i = l}^{n}\left( {f_{s_{i}} - {\overset{\_}{f}}_{s_{i}}} \right)^{2}}\sqrt{\sum_{i = 1}^{n}\left( {f_{\;_{t_{i}}} - {\overset{\_}{f}}_{t_{i}}} \right)^{2}}}} & \left( {{Eq}.\mspace{11mu} 3} \right)\end{matrix}$

where f _(s) _(i) and f _(t) _(i) are the means of the feature vectorsf_(s) _(i) and f_(t) _(i) over the time period, respectively. Othermethods for determining similarity are possible, including standardEuclidean distance and a maximum difference of the individual features.

Subsequently, based on the correlation value, a determination is made asto whether a correlation exists (block 146). In one embodiment, apredetermined threshold can be applied to the correlation value and ifthe correlation value satisfies the threshold, the doors are correlated.In a further embodiment, the correlation values can be plotted on agraph for a pair of door for a visual determination of correlation. FIG.11 is a block diagram showing, by way of example, two separate graphs150 a, 150 b each representative of correlation data for a pair ofdoors. A first graph 150 a represents correlation data for doors 1 and4, while a second graph 150 b represents correlation data for doors 1and 7. Each graph lists time, such as days, along an x-axis 151 a, 151 band correlation values along a y-axis 152 a, 152 b. The correlationvalues for each door pair are plotted as data points 153 in theirrespective graphs 150 a, 150 b.

A comparison of the graphs provides important information regarding asimilarity of the doors compared, trends in the correlation data, andoutliers. With respect to the graphs of FIG. 11, doors 1 and 4 are lesssimilar to each other as compared to doors 1 and 7, as indicated by thelower correlation values for doors 1 and 4. The lower correlation valuesfor doors 1 and 4 are likely to the anomalous motor temperature readingsof door 4, as described above with respect to FIGS. 8 and 9. Also, thecorrelation values appear to be lower towards the middle of the graph,around 150 days to 250 days, which indicates a seasonal effect since thedays in the middle of the graph represent colder weather months and thecorrelation values begin to increase during the warmer weather months.As described above with respect to FIG. 9, opening of the doors mayfunction differently in cold weather and require more work to open,which is indicated by the graphs 151 a, 151 b. Additionally, outliers154 can be observed in the graphs 150 a, 150 b. The outliers are valuesthat deviate from the other values of the graph enough to indicate thata door may be experiencing a problem or fault. Further inspection of thedoors may be necessary when outliers are detected, as further describedbelow with reference to FIG. 13.

The ability to identify similar doors can help increase efficiency ofscheduled maintenance practices by eliminating a need to spend time ondoors that do not require maintenance, even though maintenance may berecommended for those doors by the vehicle manufacturer. For example,for scheduled maintenance, maintenance personnel generally need to spendtime to inspect the interior of a door operating mechanism and thendecide what maintenance actions are necessary. The maintenance actionscan include tightening bolts, and cleaning the various parts. Othertypes of maintenance actions are possible. FIG. 12 is a flow diagramshowing, by way of example, a process 160 for efficiently schedulingmaintenance appointments. To identify doors that do and do not requiremaintenance, all doors are input (block 161) on an initial inspectionlist. A door on the initial inspection list is inspected (block 162) anda determination (block 163) is made as to whether maintenance isrequired for the door.

If maintenance is required, then the maintenance can be performed (block169) and a next door on the list is selected (block 168) for inspection.However, if no maintenance is required, then a similarity is determined(block 164) between the inspected door and all the other doors on theinitial inspection list as a correlation value, as described above indetail with respect to FIG. 10. A predetermined threshold is thenapplied (165) to the correlation values. The threshold can be computedbased on past historical evidence and measurements, which can becollected, for example, whenever a door panel is opened and a conclusionis made that no maintenance is required. Other thresholds are possible.For example, data for the door for which no maintenance is needed shouldbe recorded as historical evidence. In one example, if the historyincludes N doors, then the correlation value can be computed between allN(N−1)/2 pairs of doors. An average of the correlation values can thenbe calculated as the threshold.

The doors with correlation values that exceed (block 166) the thresholdare removed (167) from the initial inspection list, as needing noinspection. The next door on the initial inspection list is thenselected (block 168) for inspection. Alternatively, if the correlationvalues for the doors do not exceed the threshold, then the doors remain(block 170) on the initial inspection list.

In addition to identifying doors for current inspection, being able toidentify future door conditions can help prevent unexpected failure.Determining outliers can help detect events that may signify a futuredoor failure, or identify doors that need maintenance or furtherinspection. In one embodiment, Robust Principal Component Analysis canbe used for outlier detection; however, other methods for detectingoutliers are possible. FIG. 13 is a flow diagram showing, by way ofexample, a process 180 for detecting outliers. A feature vector isgenerated (block 181) for each open and close cycle of a door within adataset and each element of the feature vectors can be normalized (block182), both of which are described above with respect to FIG. 10. Adistance threshold is set (block 183) to define outliers and a selectedoutlier detection algorithm identifies (block 184) the outliers based onthe distance threshold.

The outliers can be used to predict failure of a component. FIG. 14 is aflow diagram showing, by way of example, a process 190 for predictingfailure of a door. Outliers that are different enough from known normaland faulty data to warrant further inspection are selected (block 191).The outliers can be determined using the process described above withrespect to FIG. 13. During outlier detection, a tradeoff exists betweenan inspection effort of the doors and a risk of unscheduled failure. Thetradeoff is implemented by setting a predetermined threshold on theoutlier scores. Specifically, the threshold used to identify theoutliers can originally be set high, such that less than one percent ofevents are classified as outliers. Then, the threshold can be optimizedbased on field data similar to setting the threshold for determiningsimilar doors.

The data associated with the outliers is manually inspected (block 192)using, for example, a dashboard application that runs on a computer toallow a maintenance technician to visually analyze the data. Based onthe manual inspection, a subset of the outlier data can be selected(block 193) for further inspection. The doors corresponding to theselected subset of outlier data can be inspected (block 194) fordefects.

In a further embodiment, outlier detection can also be used to identifydoors most in need of maintenance or inspection. For example, amaintenance budget allows inspection of only five doors pertransportation vehicle. The five doors that are in the worst conditionsshould be selected for inspection and any necessary maintenance. Thefive doors can be identified via outlier detection.

The operational data for a door can also be used to identify long termtrends of a door component. The trends are linked to usage and can helppredict future faults of a door. As described above, some features aremore predictive of particular faults than other features. A model forsuch predictive feature can be fit as linear in a number of the dooropenings and in the motor temperature, as provided below:

$\begin{matrix}{{{Predictive}\mspace{14mu}{feature}} = {{B\; 0} + {B\; 1\left( {{door}\mspace{14mu}{open}\mspace{14mu}{cycle}\mspace{14mu}{number}} \right)} + {B\; 2\left( {{average}\mspace{14mu}{outside}\mspace{14mu}{{temp}.\mspace{14mu}{over}}\mspace{14mu}{open}\text{/}{close}\mspace{14mu}{cycle}} \right)}}} & \left( {{Eq}.\mspace{14mu} 4} \right)\end{matrix}$

Coefficients B1 and B2 represent relative sensitivities of thepredictive feature to the number of door openings and average outsidetemperature, respectively. The coefficient B0 is a static term andrepresents the value of the predictive feature for a new unused door at0 degrees Celsius. Each of the coefficients can be determined for allthe doors using a robust linear regression, as well as other methods.Determination of long terms trends, as described above, shows that usagehas an effect on a condition of a door.

Identifying future faults can be useful for accurately predicting when aparticular fault may occur so that maintenance can be performed. FIG. 15is a flow diagram showing, by way of example, a process 210 for reducingunnecessary maintenance based on predictive faults. A model withpredictive trends is built (block 211) to predict component failures.The model is applied (block 212) to one or more features of thecomponent and can be mapped on a graph. A threshold is applied to themodeled feature (block 213) and an optimal interval for scheduledmaintenance is determined (block 213).

In one example, identification of future faults is used for schedulingcondition-based maintenance, instead of relying on manufacturerrecommendations for scheduling. FIG. 16 is a block diagram showing, byway of example, a graph 220 of average predicted values for a componentfeature over time. Time is located along an x-axis 221 of the graph 220,while values for the feature are located along a y-axis 222. Values forthe component predicted over time are averaged and plotted along a curve213 on the graph 220. Along the x-axis, T_(scheduled) 228 is identifiedas the optimal interval for scheduled maintenance based on factors otherthan the actual condition of the component, such as recommendations froma manufacturer or mechanic. However, the recommendations are oftenoverly cautious resulting in maintenance when none is required. Thus, toprevent unnecessary maintenance, future maintenance, which is based onactual conditioning of the door and future trends, is used to extend thescheduled maintenance interval for time beyond T_(scheduled) without ahigh risk of component failure.

To determine the extended maintenance schedule, a probabilitydistribution of time when the feature value will cross the threshold isdetermined. When the curve 223 for the feature value crosses thethreshold, a maintenance action may be necessary. A time at which thefeature curve will cross the threshold can be determined. However, sincethe curve represents an average of predicted values for the feature,uncertainty exists for the time when the curve crosses the threshold.The curve 226 above the threshold identifies a probability distributionof the time when the feature's value is expected to cross the threshold.A mean of the probability distance is at T₀ 229 and has a standarddistribution of σ. To ensure that failure does not occur beforemaintenance is performed, a time period prior to T₀ is selected. Theextended maintenance schedule can then be set automatically, or by anindividual associated with the transportation vehicle. In one example,the extended time period can be set at T₀-3σ 230. Other time periods forscheduling the extended maintenance are possible between T_(scheduled)and T₀. At a minimum, the extended scheduled time should be prior to T₀to ensure that maintenance is provided prior to the estimated failure.

Thus, each feature, such as a loose bolt or low grease, is analyzed fora component to determine when maintenance is needed for the particularfeature. For example, based on current usage patterns, a loose bolt maybe predicted to occur in two years, while the level of grease may becometoo low within the next six months.

An overall system model can be generated for all the models generatedfor each feature associated with one or more components in thetransportation vehicle to impose an overall maintenance strategy.Commercial tools, such as BlockSim, can be used to generate the overallsystem model.

Although the above diagnostic and prediction of conditions has beenidentified with respect to transportation vehicle doors, othercomponents are possible, including a switch machine for railway tracks.Additionally, the diagnostic and prediction analyses can be applied toautomatic doors in a building, or elevators and escalators. At aminimum, measurements of motor current across time must be available.

While the invention has been particularly shown and described asreferenced to the embodiments thereof, those skilled in the art willunderstand that the foregoing and other changes in form and detail maybe made therein without departing from the spirit and scope of theinvention.

What is claimed is:
 1. A method for determining vehicle componentconditions, further comprising: building a predictive model for avehicle component; mapping values for a feature of the vehicle componentusing the model; applying a threshold to the mapped values; predictingan occurrence of a fault of the vehicle component when one or more ofthe mapped values exceeds the threshold; and identifying an extendedoptimal interval during which the fault is predicted to occur.
 2. Amethod according to claim 1, comprising: identifying a probabilitydistribution of time when the mapped values will exceed the threshold;and calculating a mean of the probability distribution; and predictingthe mean as the time at which the fault of the vehicle component willoccur.
 3. A method according to claim 2, wherein the extended optimalinterval for maintenance comprises a time period between a manuallyscheduled maintenance at a time T_(scheduled) for the vehicle componentand a time T₀ at which the fault of the vehicle component is predictedto occur.
 4. A method according to claim 3, further comprising:scheduling the maintenance during the extended optimal interval.
 5. Amethod according to claim 4, wherein a time of the manually scheduledmaintenance is extended to a time during the extended optimal interval.6. A method according to claim 3, further comprising: scheduling themaintenance at a time of T₀-3σ.
 7. A method according to claim 1,further comprising: recommending maintenance of the vehicle componentbefore the fault of the vehicle component is predicted to occur.
 8. Amethod according to claim 1, wherein the predictive model is based onone or more factors comprising time, temperature, and usage.
 9. A methodaccording to claim 1, wherein the fault comprises one or more of anobject stuck in the vehicle component, an obstacle in a bottom rail ofthe vehicle component, bending or deformity of the bottom rail, loosebolts, the presence of dirt, and a lack of grease.
 10. A methodaccording to claim 1, wherein the features comprise one or more of amean value of the motor current during a state of the vehicle component,a highest value of the motor current in that state, a lowest value ofthe motor current for that state, an amount of variation of the motorcurrent from the mean value, a time span for the state, a mean of themotor current during opening of the vehicle component, a mean of themotor current during closing of the vehicle component, an averageoutside temperature, and an average temperature of the motor.
 11. Asystem for determining vehicle component conditions, further comprising:a database to store a predictive model for a vehicle component; and aserver comprising memory and a processor, wherein the computer processoris configured to perform the following: map values for a feature of thevehicle component using the model from the database; apply a thresholdto the mapped values; predict an occurrence of a fault of the vehiclecomponent when one or more of the mapped values exceeds the threshold;and identify an extended optimal interval during which the fault ispredicted to occur.
 12. A system according to claim 11, wherein thecomputer processor performs the following: identifying a probabilitydistribution of time when the mapped values will exceed the threshold;and calculating a mean of the probability distribution; and predictingthe mean as the time at which the fault of the vehicle component willoccur.
 13. A system according to claim 12, wherein the extended optimalinterval for maintenance comprises a time period between a manuallyscheduled maintenance at a time T_(scheduled) for the vehicle componentand a time T₀ at which the fault of the vehicle component is predictedto occur.
 14. A system according to claim 13, wherein the computerprocessor schedules the maintenance during the extended optimalinterval.
 15. A system according to claim 14, wherein a time of themanually scheduled maintenance is extended to a time during the extendedoptimal interval.
 16. A system according to claim 13, wherein thecomputer processor schedules the maintenance at a time of T₀-3σ.
 17. Asystem according to claim 11, wherein the computer processor recommendsmaintenance of the vehicle component before the fault of the vehiclecomponent is predicted to occur.
 18. A system according to claim 11,wherein the predictive model is based on one or more factors comprisingtime, temperature, and usage.
 19. A system according to claim 11,wherein the fault comprises one or more of an object stuck in thevehicle component, an obstacle in a bottom rail of the vehiclecomponent, bending or deformity of the bottom rail, loose bolts, thepresence of dirt, and a lack of grease.
 20. A system according to claim11, wherein the features comprise one or more of a mean value of themotor current during a state of the vehicle component, a highest valueof the motor current in that state, a lowest value of the motor currentfor that state, an amount of variation of the motor current from themean value, a time span for the state, a mean of the motor currentduring opening of the vehicle component, a mean of the motor currentduring closing of the vehicle component, an average outside temperature,and an average temperature of the motor.